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A Pattern Recognition Method of Fatigue Crack Growth on Metal using Acoustic Emission  

Lee, Soo-Ill (School of Electrical Engineering and Computer Science, KAIST)
Lee, Jong-Seok (School of Electrical Engineering and Computer Science, KAIST)
Min, Hwang-Ki (School of Electrical Engineering and Computer Science, KAIST)
Park, Cheol-Hoon (School of Electrical Engineering and Computer Science, KAIST)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.3, 2009 , pp. 125-137 More about this Journal
Abstract
Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems used in service. For reliable fault monitoring related to the crack growth, it is important to identify the dynamical characteristics as well as transient crack-related signals. Widely used methods which are based on physical phenomena of the three damage stages for detecting the crack growth have a problem that crack-related acoustic emission activities overlap in time, therefore it is insufficient to estimate the exact crack growth time. The proposed pattern recognition method uses the dynamical characteristics of acoustic emission as inputs for minimizing false alarms and miss alarms and performs the temporal clustering to estimate the crack growth time accurately. Experimental results show that the proposed method is effective for practical use because of its robustness to changes of acoustic emission caused by changes of pressure levels.
Keywords
acoustic emission; fatigue crack growth; neural network; Gaussian mixture model; temporal clustering;
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